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Empowering Operators and Frontline Process Engineers with AI-Powered Operational Analytics

Today’s industries face mounting challenges in assuring the best operational performance of assets, both mechanical and process. Managing asset performance is the key element of controlling the operational risk that varying situations can present.

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Making Profitable Digital Strides in Batch Processing: How to See ROI in Months When the Future is Unpredictable

Regardless of what our plans were in January 2020, nothing is the same in 2021 or the future. A digital strategy is no longer limited to a subset of impacted functions, but touches everyone’s goals organization-wide in some way, shape or form.

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インダストリアルIoTインサイト: Asset Healthスコア

この記事では AspenTech AIoT platform のIoTアナリティックスが、センサや稼働中デバイス の時系列データを使用し、設備資産のAsset Health スコアをどのように算出しているかを解説します。Asset Health スコアによって、サービスチームはリアルタイムで設備資産の健全性を把握で き、サービス業務やメンテナンスにかかる費用の削減につなげることができます。

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Low-Touch Machine Learning is Fulfilling the Promise of Asset Performance Management

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.

White Paper

Low-Touch Machine Learning is Fulfilling the Promise of Asset Performance Management

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. This white paper describes five best practices for driving state-of-the-art reliability management to predict breakdowns months in advance—increasing production and profitability.

White Paper

低接触式机器学习助力实现资产绩效管理

单独的传统预防型维护无法解决非预期停机问题。凭借低接触式机器学习所驱动的资产绩效管理,现在可能会从数十种程序、资产和维护数据中抽取相关数值,从而优化资产绩效。在本白皮书中,将学习这种插断性技术如何部署精确性故障模式识别,其具有较高的准确性,可以提前预测设备停机月数,并就约定的维护提供相关建议。本白皮书亦列述了驱动先进可靠性管理的五个最佳实践,以期增产提盈。

White Paper

資料:手軽な機械学習が資産パフォーマンス管理(APM)の可能性を開く(日本語)

従来の対処的メンテナンスだけでは、不測の事態に対応できません。手軽な機械学習による資産パフォーマンス管理(APM) により、今や製造工場のスタッフが、何十年にもわたって蓄積してきた設計や運用データから容易に価値を引き出し、資産(主に装置などのハードウェア)のパフォーマンスをより適切に管理して最適化することが可能になりました。本書では、常識を覆すような画期的なテクノロジーがどのように精密な故障パターン認識を使い、高精度に装置の故障を数ヶ月も前に予測し、処方的なメンテナンスをガイダンスするかを説明しています。また、5つのベストプラクティス(運用方法)をご紹介し、最先端の信頼性管理による、さらなる生産効率および利益率の改善手法について述べています。

White Paper

Low-Touch Machine Learning is Fulfilling the Promise of Asset Performance Management

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.

White Paper

Use Advanced Simulation to Improve Processes Involving Solids

Whether particles are being formed, reduced in size, enlarged, participating in reactions, or just being separated from a fluid stream, ignoring or poorly modeling the solids processing steps may lead to lost opportunities, including cost reductions and quality improvements. The main challenges that arise when optimizing or troubleshooting a solids process include inefficient designs due to separate modeling of fluids and solids sections, overdesign of equipment, high-energy demands, reduced yields and quality variability. Modeling the solids section of a process is important for many common processes including specialty chemicals, agrochemicals, metals and mining, pharmaceuticals, biofuels and more. This paper describes the approach for incorporating granular solids and the corresponding solids processing steps when modeling processes.

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Making Capital Project Management Decisions: Minimize Risk, Maximize Profitability

Making big capital project management decisions shouldn’t be left to subjective perceptions or over-simplified analysis. Decision-makers need quantifiable, trustworthy answers to make the most profitable decisions possible. Aspen Fidelis Reliability is a robust RAM analysis tool that can handle the real-world challenges of today's process industries. In this paper, learn how Fidelis enables you to maximize the economics of business decisions and accurately predict future asset performance of the whole system.

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